



'''simple docstring'''






from pathlib import Path

import numpy as np
from PIL import Image
def  lowerCamelCase__			(		_A							):
  a  ,      a  ,      a		:   List[Any]	  =	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.2989 * r + 0.5870 * g + 0.1140 * b
def  lowerCamelCase__			(		_A							):
  return (gray > 127) & (gray <= 255)
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   Union[str, Any]	  =	np.zeros_like(_A							)
  a		:   Union[str, Any]	  =	np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)							)

  # Copy image to padded image
  a		:   Union[str, Any]	  =	image

  # Iterate over image & apply kernel
  for x in range(image.shape[1]							):
    for y in range(image.shape[0]							):
      a		:   int	  =	(
          kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
      ).sum()
      a		:   Optional[Any]	  =	int(summation > 0							)
  return output


if __name__ == "__main__":
     # read original image
     lowerCAmelCase:   List[Any]					=		Path(__file__).resolve().parent / 'image_data' / 'lena.jpg'
     lowerCAmelCase:   List[Any]					=		np.array(Image.open(lena_path))
     # kernel to be applied
     lowerCAmelCase:   Optional[int]					=		np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
     lowerCAmelCase:   int					=		dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
     # Save the output image
     lowerCAmelCase:   Optional[Any]					=		Image.fromarray(output).convert('RGB')
     pil_img.save('result_dilation.png')




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase:   Dict					=		{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Optional[int]					=		[
         'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ViTMAEForPreTraining',
         'ViTMAELayer',
         'ViTMAEModel',
         'ViTMAEPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   List[Any]					=		[
         'TFViTMAEForPreTraining',
         'TFViTMAEModel',
         'TFViTMAEPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_vit_mae import (
              VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
              ViTMAEForPreTraining,
              ViTMAELayer,
              ViTMAEModel,
              ViTMAEPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
     import sys

     lowerCAmelCase:   Tuple					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)